Linear Equations

We use the standard notation for a system of simultaneous
linear
equations:

where

where the columns of

If *A* is upper or lower triangular, (2.4) can be solved by a
straightforward
process of backward or forward substitution.
Otherwise, the solution is obtained after first factorizing *A* as a
product of
triangular matrices (and possibly also a diagonal matrix or permutation
matrix).

The form of the factorization depends on the properties of the matrix
*A*.
LAPACK provides routines for the following types of matrices, based on
the stated factorizations:

**general**matrices (*LU*factorization with partial pivoting):

*A*=*PLU*

where*P*is a permutation matrix,*L*is lower triangular with unit diagonal elements (lower trapezoidal if*m*>*n*), and U is upper triangular (upper trapezoidal if*m*<*n*).**general band**matrices including**tridiagonal**matrices (*LU*factorization with partial pivoting): If*A*is*m*-by-*n*with*kl*subdiagonals and*ku*superdiagonals, the factorization is

*A*=*LU*

where*L*is a product of permutation and unit lower triangular matrices with*kl*subdiagonals, and*U*is upper triangular with*kl*+*ku*superdiagonals.**symmetric and Hermitian positive definite**matrices including**band**matrices (Cholesky factorization):

where*U*is an upper triangular matrix and*L*is lower triangular.**symmetric and Hermitian positive definite tridiagonal**matrices (*L D**L*^{T}factorization):

where*U*is a unit upper bidiagonal matrix,*L*is unit lower bidiagonal, and*D*is diagonal.**symmetric and Hermitian indefinite**matrices (symmetric indefinite factorization):

where*U*(or*L*) is a product of permutation and unit upper (lower) triangular matrices, and*D*is symmetric and block diagonal with diagonal blocks of order 1 or 2.

The factorization for a general tridiagonal matrix is like that for
a general band matrix with *kl* = 1 and *ku* = 1. The factorization
for a symmetric positive definite band matrix with *k* superdiagonals
(or
subdiagonals) has the same form as for a symmetric positive definite
matrix, but the factor *U* (or *L*) is a band matrix with *k*
superdiagonals (subdiagonals). Band matrices use a compact band
storage scheme described in section 5.3.3.
LAPACK routines are also provided for symmetric matrices (whether
positive definite or indefinite) using **packed** storage,
as described in section 5.3.2.

While the primary use of a matrix factorization is to solve a system of equations, other related tasks are provided as well. Wherever possible, LAPACK provides routines to perform each of these tasks for each type of matrix and storage scheme (see Tables 2.7 and 2.8). The following list relates the tasks to the last 3 characters of the name of the corresponding computational routine:

**xyyTRF:**- factorize (obviously not needed for triangular matrices);
**xyyTRS:**- use the factorization (or the matrix
*A*itself if it is triangular) to solve (2.5) by forward or backward substitution; **xyyCON:**- estimate the reciprocal of the condition number
;
Higham's modification [63] of Hager's method [59]
is used to estimate |A
^{-1}|, except for symmetric positive definite tridiagonal matrices for which it is computed directly with comparable efficiency [61]; **xyyRFS:**- compute bounds on the error in the computed solution (returned
by the xyyTRS routine), and
refine the solution to reduce the backward error (see below);
**xyyTRI:**- use the factorization (or the matrix
*A*itself if it is triangular) to compute*A*^{-1}(not provided for band matrices, because the inverse does not in general preserve bandedness); **xyyEQU:**- compute scaling factors to equilibrate
*A*(not provided for tridiagonal, symmetric indefinite, or triangular matrices). These routines do not actually scale the matrices: auxiliary routines xLAQyy may be used for that purpose -- see the code of the driver routines xyySVX for sample usage.

Note that some of the above routines depend on the output of others:

**xyyTRF:**- may work on an equilibrated matrix produced by
xyyEQU and xLAQyy, if yy is one of {GE, GB, PO, PP, PB};
**xyyTRS:**- requires the factorization returned by xyyTRF;
**xyyCON:**- requires the norm of the original matrix
*A*, and the factorization returned by xyyTRF; **xyyRFS:**- requires the original matrices
*A*and*B*, the factorization returned by xyyTRF, and the solution*X*returned by xyyTRS; **xyyTRI:**- requires the factorization returned by xyyTRF.

The RFS (``refine solution'') routines perform iterative
refinement
and compute backward and forward error bounds for the solution.
Iterative refinement is done in the same precision as the input data.
In particular, the residual is *not* computed with extra precision,
as has been traditionally done.
The benefit of this procedure is discussed in Section 4.4.